CVE-2026-47471
Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.
NVD · uneditedNVIDIA TensorRT-LLM for any platform contains a vulnerability in tensor deserialization, where an attacker could cause a heap based buffer overflow. A successful exploit of this vulnerability might lead to information disclosure, data tampering, or denial of service.
Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.
dbcve analysis · moderate confidenceNVIDIA TensorRT-LLM contains a heap-based buffer overflow vulnerability in its tensor deserialization functionality. During the loading or deserialization of tensor data, the application fails to properly validate buffer sizes before writing data, allowing an attacker to overflow heap memory boundaries. This could enable information disclosure through memory leakage, data tampering by corrupting adjacent heap structures, or denial of service via application crash.
Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.
CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.
From the vector- Attack vector
- Adjacent
- Complexity
- High
- Privileges
- None
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- High
CVSS:3.1/AV:A/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:H
Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.
dbcve checksWork through these to decide whether this CVE applies to you.
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Confirm TensorRT-LLM installationRun 'pip show tensorrt-llm' or check for the tensorrt_llm Python package in your environmentAffected if The package is not installed or the command fails, indicating TensorRT-LLM is not present
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Identify installed TensorRT-LLM versionExecute 'pip show tensorrt-llm' and note the Version field, or import tensorrt_llm and print(tensorrt_llm.__version__)Affected if A version is returned that falls within any affected version range (compare your version to vendor advisories)
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Verify tensor loading from external sourcesReview application logs, code, or configuration for calls to tensor loading functions such as 'fromfile', 'load', 'deserialize', or TensorRT tensor deserialization APIs when loading models from untrusted pathsAffected if The application loads tensors or model files from untrusted or user-controlled sources without validation
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Check for deserialization of untrusted tensor dataInspect any model loading pipelines, inference scripts, or custom operators that deserialize tensors, and verify if the data source is from trusted locations onlyAffected if Tensor deserialization is performed on data from untrusted or network-accessible sources
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Audit memory allocation for tensor operationsEnable memory debugging tools such as Valgrind or address sanitizer (ASAN) during tensor loading operations to detect heap buffer overflowsAffected if Heap buffer overflows are detected during tensor deserialization or model loading
Your environment is affected if TensorRT-LLM is installed and you load or deserialize tensors from untrusted sources without validating buffer sizes first.
Generated from the published advisory. Verify against your own configuration.
Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.
From vendor dataApply any vendor-supplied patches or updates to TensorRT-LLM when released by NVIDIA. In the interim, implement strict input validation on any model or tensor data before deserialization, restrict model loading to trusted sources only, and consider deploying memory protection mechanisms such as sandboxing or runtime application self-protection (RASP) to detect heap corruption patterns.
- Consultation4.0 h
- Implementation8.0 h
- Testing6.0 h
- Review / QA3.0 h
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Free · runs locallyCheck whether your project pulls in CVE-2026-47471 — or any other known-vulnerable package — straight from your lock files. Free and open source; it runs locally and uploads nothing.
References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.
Primary sourcesPractitioner notes
ContributedPeer-ranked notes from engineers who’ve handled CVE-2026-47471 in production — separate from our analysis above.
The advisory tells you what broke. It rarely tells you what actually worked. If you’ve dealt with this one, that detail is what the next engineer is searching for.
- The version that genuinely resolved it — not the one the vendor claimed
- A config change or rule that shut the vector down
- A gotcha in the upgrade path that cost you an afternoon
No notes yet
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- Verified mitigations, workarounds, and config changes
- Version or environment caveats, and links to real fixes
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